Early diagnosis is essential for prompt care and intervention of neurodegenerative disorders including Parkinson’s and Alzheimer’s. In this study, we propose a novel approach utilizing multiband nonlinear EEG analysis for the early diagnosis of these diseases. By extracting and analyzing brain signals across multiple frequency bands, along with nonlinear features, we aim to uncover subtle but significant alterations indicative of disease onset. Our findings suggest promising potential for accurately detecting Alzheimer’s and Parkinson’s diseases at an early stage, paving the way for improved prognosis and targeted therapeutic interventions. The graph indicates that BPNN achieved the highest accuracy among these algorithms, achieving 95.5%. The system bridges gap in healthcare, offering timely diagnoses for Alzheimer’s and Parkinson’s, fostering a more inclusive and proactive social support system.